Interpretive Summary: Automated body condition scoring (BCS) from digital images has been demonstrated to be feasible; and commercial technologies are being developed. The primary objective of this research was to identify the factors that influence the potential profitability of investing in an automated BCS system. An expert opinion survey was conducted to provide estimates for potential improvements associated with adopting this technology. The experts indicated that the most important benefit of automated BCS would be disease reduction followed by nutritional cohort management, reproduction, animal well-being, energy efficiency, and genetics in order. A stochastic simulation model of a dairy system, designed to assist dairy producers with investment decisions for precision dairy farming technologies was utilized to perform an economic analysis, Net Present Value (NPV) analysis. Benefits of technology adoption were estimated through assessment of the impact of BCS on the incidence of ketosis, milk fever, and metritis, conception rate at first service, and energy efficiency. Improvements in reproductive performance had the largest influence on revenues followed by energy efficiency and then by disease reduction. As the technology matures, additional knowledge with regard to the specific benefits from frequent BCS will be gained which could improve estimates in the investment decision model. This investment decision can be analyzed by farmers, economists, and financial institutions with input of herd-specific values using this model.

Technical Abstract:
Automated body condition scoring (BCS) through extraction of information from digital images has been demonstrated to be feasible; and commercial technologies are being developed. The primary objective of this research was to identify the factors that influence the potential profitability of investing in an automated BCS system. An expert opinion survey was conducted to provide estimates for potential improvements associated with technology adoption. The experts indicated that the most important benefit of automated BCS would be disease reduction followed by nutritional cohort management, reproduction, animal well-being, energy efficiency, and genetics in order. A stochastic simulation model of a dairy system, designed to assist dairy producers with investment decisions for Precision Dairy Farming technologies was utilized to perform a Net Present Value (NPV) analysis. Benefits of technology adoption were estimated through assessment of the impact of BCS on the incidence of ketosis, milk fever, and metritis, conception rate at first service, and energy efficiency. Improvements in reproductive performance had the largest influence on revenues followed by energy efficiency and then by disease reduction. The impact of disease reduction was less than anticipated because the ideal BCS indicated by experts resulted in a simulated increase in the proportion of cows with BCS at calving = 3.50. The estimates for disease risks and conception rates, obtained from literature, however, suggested that this increase would result in increased disease incidence. Stochastic variables that had the most influence on NPV were as follows: variable cost increases after technology adoption; the odds ratios for ketosis and milk fever incidence and conception rates at first 25 service associated with varying BCS ranges; uncertainty of the impact of ketosis, milk fever, and metritis on days open, unrealized milk, veterinary costs, labor, and discarded milk; and the change in the percent of cows with BCS at calving = 3.25 before and after technology adoption. The deterministic inputs impacting NPV were herd size, management level, and level of milk production. Investment in this technology may be profitable but results were very herd specific. A simulation modeling a deterministic 25% decrease in the percent of cows with BCS at calving = 3.25 demonstrated a positive NPV in 86.6% of 1000 iterations. As the technology matures, additional knowledge with regard to the specific benefits from frequent BCS will be gained which could improve estimates in the investment decision model. This investment decision can be analyzed with input of herd-specific values using this model.